MétaCan
Menu
Back to cohort
Record W2080703619 · doi:10.1038/srep08780

Spatio-temporal behaviour of atomic-scale tribo-ceramic films in adaptive surface engineered nano-materials

2015· article· en· W2080703619 on OpenAlexaff
German Fox‐Rabinovich, А. И. Ковалев, Stephen C. Veldhuis, Kenji Yamamoto, J.L. Endrino, Иосиф Гершман, Alexander Rashkovskiy, Myriam H. Aguirre, Dmitry Wainstein

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsMcMaster University
FundersRussian Science FoundationRussian Foundation for Basic Research
KeywordsCeramicMaterials scienceNanotechnologyNano-Nanoscopic scaleScale (ratio)Surface (topology)Atomic unitsComputer scienceComposite materialPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Atomic-scale, tribo-ceramic films associated with dissipative structures formation are discovered under extreme frictional conditions which trigger self-organization. For the first time, we present an actual image of meta-stable protective tribo-ceramics within thicknesses of a few atomic layers. A mullite and sapphire structure predominates in these phases. They act as thermal barriers with an amazing energy soaking/dissipating capacity. Less protective tribo-films cannot sustain in these severe conditions and rapidly wear out. Therefore, a functional hierarchy is established. The created tribo-films act in synergy, striving to better adapt themselves to external stimuli. Under a highly complex structure and non-equilibrium state, the upcoming generation of adaptive surface engineered nano-multilayer materials behaves like intelligent systems - capable of generating, with unprecedented efficiency, the necessary tribo-films to endure an increasingly severe environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.266
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicDiamond and Carbon-based Materials ResearchFrench-language works237,207